Boqing Gong

Research ScientistGoogle

Biography

Boqing Gong is a research scientist at Google, Seattle and a principal investigator at ICSI, Berkeley. His research lies at the intersection of machine learning and computer vision and has been focusing on data- and label-efficient learning (e.g., domain adaptation, few-shot, reinforcement, webly-supervised, and self-supervised learning) and the visual analytics of objects, scenes, human activities, and their attributes. Before joining Google in 2019, he worked in Tencent and was a tenure-track Assistant Professor at the University of Central Florida (UCF). He received an NSF CRII award in 2016 and an NSF BIGDATA award in 2017, both of which were the first of their kinds ever granted to UCF. He is/was an area chair of NeurIPS 2019, ICCV 2019, ICML 2019, AISTATS 2019, WACV 2019, and WACV 2018. He received his Ph.D. in 2015 at the University of Southern California, where the Viterbi Fellowship partially supported his work.

Area chair of NeurIPS'19, ICML'19, ICCV'19, and AISTATS'19 (02/2019)

NSF panelist (2019)

Nine Papers Published in 2018

12/2018: Talk at IEEE BIGDATA Workshop on Big Data Transfer Learning

11/2018: Talk at INFORMS Special Session on Stochastic Optimization Methods and Approximation Theory in Machine Learning

"The Multiple Shades of Dropout for Discriminative and Generative Deep Neural Networks". Dropout, which independently zeros out the outputs of neurons at random, has become one of the most popular techniques in training deep neural networks due to its simplicity and remarkable effectiveness. This talk reveals multiple shades of dropout for both discriminative and generative deep neural networks, mainly covering our following works: [Li et al., NIPS'16] and [Wei, Gong, et al., ICLR'18].